alt text

Project: Investigate Medical Appointment No Shows

Table of Contents

Introduction

This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show upfor their appointment. A number of characteristics about the patient are included in each row

Questions to ask:

  1. What is the probability that patients missed their scheduled appointments?
  2. Does patients age range has effect for absence?
  3. Does different neighborhoods have an effect in absence ratio?
  4. What other factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?
  5. Does the sms sent to patients has an effect to attending the appointment?

Data Wrangling

In these data, we have to have to filter our the data, and clean it.

General Properties

Data Cleaning (summary of dataset checkers)

  1. check for missing rows
  2. check for duplicates
  3. check for if there is any null
  4. check for columns name
  5. check for non logic values

Exploratory Data Analysis

Research Question 1 : What is the probability that patients missed their scheduled appointments?

Research Question 2 : Does patients age range has effect for absence?

Question number 3 : Does different neighborhoods have an effect in absence ratio?

Question Number 4: What other factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?

Question number 5 : Does the sms sent to patients has an effect to attending the appointment?

Conclusions

  1. Statistics showing that 20.3% patients don't show up after schedule medical appointment and 79.7% of the patients show up .
  2. study shows that patients that not show in thier appointments from the adults with percentage 47.3%.
  3. it is obvious that neighbourhood have a relation to no show as it can be hard to reach the location of the hopital or hard transportation.
  4. the study shows also that most of the patients that don't show up after schedule medical appointment are females with percentage 65.8% .
  5. logically we can think that when we send sms to patients that it will negatively effect the no show ratio but the study shows that it have no relation as when hospitals send sms's absence ratio increased.